Ozone pollution: a ‘hidden’ environmental layer for athletes preparing for the Tokyo 2020 Olympics & Paralympics
Bibliographic record
Abstract
Environmental factors such as climate and pollution form a key part of major championship preparation.1 The Tokyo 2020 Olympic/Paralympic Games will present a unique combination of high thermal and ozone stressors. Japan has the highest levels of ozone in the Organisation for Economic Co-operation and Development; with annual peak values (65–73 ppb) aligning with the Olympic/Paralympic schedules (23 July to 5 September 2021). Herein, we provide a synopsis of the effects and potential mitigating factors of air pollution on athlete health and performance. We integrate these recommendations with established guidelines on heat,1 to provide guidance for the 2020 Summer Olympic/Paralympic Games and beyond (figure 1). Figure 1 Summary of how to concurrently prepare to compete in the heat with high levels of ozone. An athlete performance and health checklist for science and medicine staff. Air pollution is a heterogeneous combination of both particles and gases that varies by location, time and season. Ground level ozone is a gas pollutant resulting from a chemical reaction between nitrogen oxides and hydrocarbons in the presence of ultraviolet radiation.2 3 Due to ozone’s positive …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".